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The discussions highlight the article's core insight that surprisal-based models underestimate the difficulty of syntactic disambiguation, with comments emphasizing the use of advanced models like RNNs and the need for mechanisms like reanalysis. Words like 'severely underestimate' and phrases such as 'new article' and 'final version' underscore their engagement and acknowledgment of the research's importance.
Most discussions acknowledge the article's findings, like the underestimation of garden-path effects by surprisal models, indicating general agreement with the publication's conclusions.
The posts show a high level of curiosity and appreciation for the research, referencing specific methods and implications, which suggests strong interest.
Some comments delve into technical details, such as the use of RNN language models and the linking hypothesis, reflecting thoughtful engagement.
Participants recognize the significance of the findings, especially the need for more complex mechanisms beyond single-stage models, indicating a moderate impact perception.
New article in Cognitive Science (w/ @tallinzen)! Peaks in surprisal predict garden paths in reading, but we show that these peaks severely underestimate the magnitude of the effects. Suggests other repair mechanisms are used when processing garden paths. https://t.co/JTt95otxP5
Great to see the final version of this work led by @marty_with_an_e! We used RNN language models to test the hypothesis that the difficulty that people experience when reading garden-path sentences can be explained as a simple word predictability effect. https://t.co/2R0yCHwbzD
@tmalsburg @mariemarm (Just because I'm riding the high of actually getting this published) @tallinzen and I explore the linking hypothesis between surprisal and reading times in our new paper. Section 2.5 is almost exactly what you ask for!
RT @marty_with_an_e: New article in Cognitive Science (w/ @tallinzen)! Peaks in surprisal predict garden paths in reading, but we show tha…
Single-stage prediction models do not explain the magnitude of syntactic disambiguation difficulty https://t.co/dYRVdWPRzK
RT @marty_with_an_e: New article in Cognitive Science (w/ @tallinzen)! Peaks in surprisal predict garden paths in reading, but we show tha…
This video explores how syntactic surprisal influences reading times by examining studies that reveal the cognitive processing involved. We delve into works that show how anticipatory effects shape our understanding of…
RT @marty_with_an_e: New article in Cognitive Science (w/ @tallinzen)! Peaks in surprisal predict garden paths in reading, but we show tha…
RT @tallinzen: Great to see the final version of this work led by @marty_with_an_e! We used RNN language models to test the hypothesis that…
RT @tallinzen: Great to see the final version of this work led by @marty_with_an_e! We used RNN language models to test the hypothesis that…
RT @marty_with_an_e: New article in Cognitive Science (w/ @tallinzen)! Peaks in surprisal predict garden paths in reading, but we show tha…
RT @marty_with_an_e: New article in Cognitive Science (w/ @tallinzen)! Peaks in surprisal predict garden paths in reading, but we show tha…
RT @marty_with_an_e: New article in Cognitive Science (w/ @tallinzen)! Peaks in surprisal predict garden paths in reading, but we show tha…
RT @marty_with_an_e: New article in Cognitive Science (w/ @tallinzen)! Peaks in surprisal predict garden paths in reading, but we show tha…
RT @marty_with_an_e: New article in Cognitive Science (w/ @tallinzen)! Peaks in surprisal predict garden paths in reading, but we show tha…
@tmalsburg @mariemarm (Just because I'm riding the high of actually getting this published) @tallinzen and I explore the linking hypothesis between surprisal and reading times in our new paper. Section 2.5 is almost exactly what you ask for!
RT @tallinzen: Great to see the final version of this work led by @marty_with_an_e! We used RNN language models to test the hypothesis that…
New article in Cognitive Science (w/ @tallinzen)! Peaks in surprisal predict garden paths in reading, but we show that these peaks severely underestimate the magnitude of the effects. Suggests other repair mechanisms are used when processing garden paths. https://t.co/JTt95otxP5
Great to see the final version of this work led by @marty_with_an_e! We used RNN language models to test the hypothesis that the difficulty that people experience when reading garden-path sentences can be explained as a simple word predictability effect. https://t.co/2R0yCHwbzD
Single-stage prediction models do not explain the magnitude of syntactic disambiguation difficulty https://t.co/dYRVdWPRzK
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